Titlebook: Learning Theory; 18th Annual Conferen Peter Auer,Ron Meir Conference proceedings 2005 Springer-Verlag Berlin Heidelberg 2005 Boosting.Suppo

充氣女

978-3-540-26556-6Springer-Verlag Berlin Heidelberg 2005

瑣事

Learning Theory978-3-540-31892-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

冷漠

On the Consistency of Multiclass Classification Methodsproperty of Bayes consistency. We provide a necessary and sufficient condition for consistency which applies to a large class of multiclass classification methods. The approach is illustrated by applying it to some multiclass methods proposed in the literature.

怒目而視

Data Dependent Concentration Bounds for Sequential Prediction Algorithmsg some newly developed probability inequalities, we are able to bound the total generalization performance of a learning algorithm in terms of its observed total loss. Consequences of this analysis will be illustrated with examples.

防銹

The Weak Aggregating Algorithm and Weak Mixabilityrom a finite alphabet. For the bounded games the paper introduces the Weak Aggregating Algorithm that allows us to obtain additive terms of the form .. A modification of the Weak Aggregating Algorithm that covers unbounded games is also described.

fastness

Tracking the Best of Many Experts provided that the set of experts has a certain structure allowing efficient implementations of the exponentially weighted average predictor. As an example we work out the case where each expert is represented by a path in a directed graph and the loss of each expert is the sum of the weights over the edges in the path.

話

https://doi.org/10.1007/b137542Boosting; Support Vector Machine; classification; game theory; learning; learning theory; supervised learn

Allure

Martingale BoostingMartingale boosting is a simple and easily understood technique with a simple and easily understood analysis. A slight variant of the approach provably achieves optimal accuracy in the presence of random misclassification noise.

custody

Sensitive Error Correcting Output CodesWe present a reduction from cost-sensitive classification to binary classification based on (a modification of) error correcting output codes. The reduction satisfies the property that . regret for binary classification implies ..-regret of at most 2. for cost estimation. This has several implications:

Brain-Imaging

Margin-Based Ranking Meets Boosting in the MiddleUC and achieves the same AUC as RankBoost. This explains the empirical observations made by Cortes and Mohri, and Caruana and Niculescu-Mizil, about the excellent performance of AdaBoost as a ranking algorithm, as measured by the AUC.